Road vehicles use tires that are compliant air-filled structures that exert force on the road by continually deforming and slipping relative to the road. For road vehicle stability and traction, modern vehicle control systems often use an estimate of how the vehicle moves with respect to the road. Accurately estimating road-relative velocity vectors is of particular importance as velocity can be used for feedback control, position estimation, detection, and intelligent response to tire nonlinearities and sliding. However, because of the compliance of the tires, directional stiffness, the radius of the tire, among other things, can vary as well as various factors such as temperature, pressure, wear, and load from the car and road, can make conventional approaches to velocity estimation challenging.
In response, safety systems have been developed that can determine the road-relative velocity using wheel encoders, inertial sensors, and Global Navigation Satellite Systems (GNSS), among others. However, the current safety systems are often insufficient and can provide inaccurate road-relative velocity. For example, inertial sensors require the integration of noisy accelerometers which can experience drift, making velocity and slip determinations challenging. GNSS systems such as Global Positioning Systems (GPS) are often unreliable and subject to jammers, which similarly makes the use of GPS challenging for road relative velocity and other computations.